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Data Scientist

Nanodegree Program

Get hands-on experience running data pipelines, designing experiments, building recommendation systems, and more.

Get hands-on experience running data pipelines, designing experiments, building recommendation systems, and more.

Built in collaboration with

IBM

Advanced

4 months

Real-world Projects

Completion Certificate

Last Updated May 31, 2024

Skills you'll learn:
scikit-learn • Interpreting test results • Smart experiments • Experiment control
Prerequisites:
Python data analysis libraries • Basic statistical modeling • Relational database basics

Courses In This Program

Course 1 1 hour

Welcome to the Data Scientist Nanodegree Program

Lesson 1

An Introduction to Your Nanodegree Program

Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.

Lesson 2

Getting Help

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

Lesson 3

The Skills That Set You Apart

Course 2 3 weeks

Introduction to Data Science

Learn the data science process, including how to build effective data visualizations, and how to communicate with various stakeholders.

Lesson 1

Welcome to the Course

This lesson will give you an overview of the course, discuss pre-requisites and stakeholders.

Lesson 2

The Data Science Process

In this lesson, you will learn about CRISP-DM and how you can apply it to many data science problems.

Lesson 3

Communicating to Stakeholders

In this lesson, you will be creating a post to communicate your findings via Medium.

Lesson 4 • Project

Project: Writing a Data Scientist Blog Post

In this project, you will create a blog post and Github repository that you can use as you build your data science portfolio.

Course 3 18 hours

Software Engineering

Software engineering skills are increasingly important for data scientists. In this course, you'll learn best practices for writing software. Then you'll work on your software skills by coding a Python package and a web data dashboard.

Lesson 1

Introduction to Software Engineering

Welcome to Software Engineering for Data Scientists! Learn about the course and meet your instructors.

Lesson 2

Software Engineering Practices Pt I

Learn software engineering practices and how they apply in data science. Part one covers clean and modular code, code efficiency, refactoring, documentation, and version control.

Lesson 3

Software Engineering Practices Pt II

Learn software engineering practices and how they apply in data science. Part two covers testing code, logging, and conducting code reviews.

Lesson 4

Introduction to Object-Oriented Programming

Learn the basics of object-oriented programming so that you can build your own Python package.

Lesson 5

Portfolio Exercise: Upload a Package to PyPi

Create your own Python package and upload your package to PyPi.

Lesson 6

Web Development

Develop a data dashboard using Flask, Bootstrap, Plotly and Pandas.

Lesson 7

Portfolio Exercise: Deploy a Data Dashboard

Customize the data dashboard from the previous lesson to make it your own. Upload the dashboard to the web.

Course 4 1 month

Data Engineering

In data engineering for data scientists, you will practice building ETL, NLP, and machine learning pipelines. This will prepare you for the project with our industry partner Figure 8.

Lesson 1

Introduction to Data Engineering

You will get an introduction to the data engineering for data scientists course and project. The lessons include ETL pipelines, natural language pipelines, and machine learning pipelines.

Lesson 2

ETL Pipelines

ETL stands for extract, transform, and load. This is the most common type of data pipeline, and you will practice each step in this lesson.

Lesson 3

NLP Pipelines

In order to complete the project at the end of the course, you will need some natural language processing skills. Here you will practice engineering machine learning features from text data.

Lesson 4

Machine Learning Pipelines

You'll use the Scikit-Learn package to code a machine learning pipeline. With these skills, you can ingest data, create features, and train a machine learning algorithm in just one step.

Lesson 5 • Project

Project: Disaster Response Pipeline

You’ll build a machine learning pipeline to categorize emergency messages based on the needs communicated by the sender.

Taught By The Best

Photo of Josh Bernhard

Josh Bernhard

Staff Data Scientist

Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.

Photo of Mike Yi

Mike Yi

Data Analyst Instructor

Mike is a content developer with a multidisciplinary academic background, including math, statistics, physics, and psychology. Previously, he worked on Udacity's Data Analyst Nanodegree program as a support lead.

Photo of Judit Lantos

Judit Lantos

Senior Data Engineer at Netflix

Judit is a Senior Data Engineer at Netflix. Formerly a Data Engineer at Split, where she worked on the statistical engine of their full-stack experimentation platform, she has also been an instructor at Insight Data Science, helping software engineers and academic coders transition to DE roles.

Photo of David Drummond

David Drummond

VP of Engineering at Insight

David is VP of Engineering at Insight where he enjoys breaking down difficult concepts and helping others learn data engineering. David has a PhD in Physics from UC Riverside.

Photo of Andrew Paster

Andrew Paster

Instructor

Andrew has an engineering degree from Yale, and has used his data science skills to build a jewelry business from the ground up. He has additionally created courses for Udacity's Self-Driving Car Engineer Nanodegree program.

Photo of Juno Lee

Juno Lee

Instructor

As a data scientist at Looplist, Juno built neural networks to analyze and categorize product images, a recommendation system to personalize shopping experiences for each user, and tools to generate insight into user behavior.

Photo of Luis Serrano

Luis Serrano

Instructor

Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

Ratings & Reviews

Average Rating: 4.8 Stars

781 Reviews

Dattaji K.

January 27, 2023

Going good although a bit slow due to time constraints.

Maxim K.

January 25, 2023

Small bugs, but the content and the tasks are really great for the job preparation!

Saad A.

January 10, 2023

great start

Nihal K.

December 28, 2022

Just Brilliant.

Kerim Kutluhan T.

December 15, 2022

It is definitely more challenging than any coursera course I have taken related to the subject and therefore I believe I am learning more. thank you

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About Data Scientist

Our Data Science Nanodegree program is a specialized data science program that equips learners with the skills to run data pipelines, design experiments, and develop recommendation systems. The curriculum, rich in practical skills like scikit-learn and NumPy, prepares you for real-world challenges in data science. Learn from experts and engage in projects that mirror industry scenarios. At Udacity, our focus is on making you job-ready with practical, industry-aligned training. Dive into the world of data science with us, where hands-on learning meets career transformation. Enhance your skills and pave your way to a successful career in data science.

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